Papers
3
Total Citations
103
H-Index
3
About
Yuanhong Li is a leading researcher in agricultural robotics and intelligent perception systems, with a primary focus on deep learning-based fruit detection for automated harvesting and yield estimation. Li’s major contributions center on developing highly accurate, real-time object detection models tailored to dense, occluded orchard environments. Notably, their work on improving the YOLOv5 and YOLOv4 architectures achieved breakthrough precision in detecting small, clustered targets like litchi and plum fruits, directly addressing the challenges of variable lighting, leaf occlusion, and overlapping fruit—critical for enabling reliable robotic harvesting. The paper “Fast and precise detection of litchi fruits for yield estimation based on the improved YOLOv5 model” has garnered 58 citations, while “Precision Detection of Dense Plums in Orchards Using the Improved YOLOv4 Model” has received 41 citations, underscoring their significant impact on precision agriculture. Additionally, Li has explored autonomous navigation for quadrotors, developing an improved artificial potential field (APF) algorithm for intercepting ground robots in dynamic environments. This interdisciplinary work demonstrates Li’s versatility in both agricultural perception and multi-robot coordination, making their research highly relevant for students and engineers advancing smart farming and autonomous systems.
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